Query performance prediction for concurrent queries using graph embedding

Abstract

Query performance prediction is vital to many database tasks (e.g., database monitoring and query scheduling). Existing methods focus on predicting the performance for a single query but cannot effectively predict the performance for concurrent queries, because it is rather hard to capture the correlations between different queries, e.g., lock conflict and buffer sharing. To address this problem, we propose a performance prediction system for concurrent queries using a graph embedding based model. To the best of our knowledge, this is the first graph-embedding-based performance prediction model for concurrent queries. We first propose a graph model to encode query features, where each vertex is a node in the query plan of a query and each edge between two vertices denotes the correlations between them, e.g., sharing the same table/index or competing resources. We then propose a prediction model, in which we use a graph embedding network to encode the graph features and adopt a prediction network to predict query performance using deep learning. Since workloads may dynamically change, we propose a graph update and compaction algorithm to adapt to workload changes. We have conducted extensive experiments on real-world datasets, and experimental results showed that our method outperformed the state-of-the-art approaches.